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The process of model ensembling can be visualized as a workflow. It begins with a single 'Prompt' being input into multiple distinct Large Language Models, such as 'LLM1' and 'LLM2'. Each model independently generates a candidate output, for instance, 'Prediction1', 'Prediction2', and 'Prediction3'. In the final step, these individual predictions are aggregated through a 'Combine/Select' mechanism to produce a single 'Final Prediction'.

Visual Diagram of Model Ensembling

A technique exists where multiple language models are used together to improve the quality of a final response. Arrange the following steps to correctly represent the workflow of this technique.

Consider a system where a single user prompt is sent to three different language models simultaneously. Each model generates its own response. These three responses are then passed to a final mechanism that selects the best one or synthesizes them into a new, single response. Based on this workflow, what is the primary advantage of this approach?

A system is designed to improve response quality by using several language models at once. Match each component of this system's workflow to its correct description.

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